Optical Fiber Communication System Based on Intelligent Joint Source-Channel Coded Modulation

• Published in: Journal of lightwave technology 2024-03, Vol.42 (6), p.2009-2017
• Main Authors: Huang, Hongyu, Cheng, Liming, Yu, Zhenming, Zhang, Wei, Mu, Yueqiu, Xu, Kun
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Codes; Coding; Communications systems; Convolutional neural networks; Decoding; Deep learning; Demodulation; Image transmission; joint source-channel coded mod- ulation(JSCCM); Modulation; Optical attenuators; Optical communication; Optical fiber amplifiers; optical fiber communication(OFC); Optical fiber networks; Optical fibers; Optical receivers; Symbols
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2023.3328311

To improve the information transmission ability of point-to-point optical fiber communication systems, we propose and experimentally demonstrate an optical fiber communication system based on intelligent joint source-channel coded modulation (JSCCM-OFC). Instead of encoding information into bits by source coding and employing channel coding, the JSCCM-OFC system codes and modulates the information source into discrete-time analog symbols jointly using deep learning. The generated discrete-time analog symbols are then directly transmitted through an optical fiber. We designed language attention and dual-attention residual networks as the JSCCM for text and image transmission, respectively. For interaction between the information layer and the optical physical layer, we incorporated a convolutional neural network into the joint demodulation and decoding (JDD) network and implemented joint optimization in the receiver. Compared with the bit-based structure, the JSCCM-OFC system achieved higher information compression and obtained a more stable performance, especially in the low-received optical power regime. Moreover, JSCCM enhanced the robustness of the system against optical link impairments.